Latest / Luke's English Podcast / New antibiotics are needed to change the game

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New antibiotics are needed to change the game · Luke's English Podcast

• 0:07
  Speaker 1: Hello, welcome to Science of the Times. I am Saima Khalid.
• 0:11
  Speaker 1: This week's episode is based on a lecture I gave
• 0:13
  Speaker 1: in Bradford City Hall at the end of May earlier
• 0:16
  Speaker 1: this year. This was the second lecture in an annual
• 0:19
  Speaker 1: series of lectures named after Ibn Sina and hosted by
• 0:22
  Speaker 1: the Muslim Institute. For those of you who are unaware
• 0:25
  Speaker 1: of who he was, Ibn Sina was a polymath. He
• 0:28
  Speaker 1: excelled in mathematics, in philosophy, most notably medicine, and it's
• 0:32
  Speaker 1: for this really that he's most well known. Ibn Sina
• 0:35
  Speaker 1: lived about a thousand years ago, during what is now
• 0:38
  Speaker 1: referred to as the Golden Age of Islam. He lived
• 0:41
  Speaker 1: in what is now modern day Iran. Okay, so now
• 0:45
  Speaker 1: with the permission of the Muslim Institute, I'm going to
• 0:48
  Speaker 1: switch over to the coverage of the lecture itself, in
• 0:51
  Speaker 1: which I will cut straight through to where I begin
• 0:54
  Speaker 1: to discuss my own research on computational microbiology, whereby I
• 0:59
  Speaker 1: use computers to try to understand how bacteria protect themselves,
• 1:03
  Speaker 1: how they keep out harmful substances, like for example, antibiotics,
• 1:08
  Speaker 1: but allowing essential nutrients like vitamins and ions that they
• 1:12
  Speaker 1: need to survive. Of course, watch this or listen to
• 1:16
  Speaker 1: this on any format you like. However, my recommendation is
• 1:19
  Speaker 1: that perhaps you use the YouTube coverage for this particular
• 1:23
  Speaker 1: episode because there are some visual elements that I referred
• 1:27
  Speaker 1: to during the lecture, which of course you will not
• 1:29
  Speaker 1: be able to see if you have audio only. Okay,
• 1:32
  Speaker 1: so let's switch straight over to the coverage of the lecture.
• 1:36
  Speaker 1: talk about at some point. And so now you can
• 1:40
  Speaker 1: see there the stark figures. 1.27 million deaths caused directly
• 1:48
  Speaker 1: by antimicrobial resistant bacteria in 2019, with a further almost
• 1:53
  Speaker 1: 5 million associated with the same issue. So these numbers
• 1:58
  Speaker 1: are going to just get worse and worse. And the
• 2:01
  Speaker 1: problem is, Nobody is trying to discover new antibiotics. There
• 2:05
  Speaker 1: are no pharmaceutical companies really working in this area. So
• 2:10
  Speaker 1: it's called the discovery gap. The last real novel antibiotic
• 2:15
  Speaker 1: was discovered over 20 years ago. There've been one or
• 2:18
  Speaker 1: two since, but it's just been tinkering with already existing ones,
• 2:21
  Speaker 1: no new classes. And the reason for that, if I'm honest,
• 2:26
  Speaker 1: the brutal biggest reason is it's hard to make money.
• 2:29
  Speaker 1: It's almost impossible to make money from making antibiotics. And
• 2:33
  Speaker 1: the reason for that is resistance develops immediately. So if
• 2:37
  Speaker 1: a new antibiotic is discovered, you will not be allowed
• 2:41
  Speaker 1: to sell it and use it because they'll say, keep
• 2:43
  Speaker 1: it for when the pandemic comes. Because the moment you
• 2:46
  Speaker 1: start using it, it stops becoming effective. The resistance begins.
• 2:50
  Speaker 1: And so that's the real driver. And so working on antibiotics, therefore,
• 2:54
  Speaker 1: is left to charities and left to academics. And it
• 2:57
  Speaker 1: really kind of, I guess, motivates a lot of the
• 3:00
  Speaker 1: work I do. Okay, so I just wanted to spend
• 3:05
  Speaker 1: a few minutes sort of explaining to you why bacteria
• 3:08
  Speaker 1: are so hard to kill. You know, you could say
• 3:10
  Speaker 1: we are complicated humans. We have many, many cells. We
• 3:14
  Speaker 1: have brains. We have eyes, nose, mouth, hands. We've made weapons.
• 3:18
  Speaker 1: We've made bombs. You know, why can't we kill a bacteria?
• 3:22
  Speaker 1: And what is so hard about it? And so ultimately
• 3:26
  Speaker 1: this is what we want to do, right? We want
• 3:27
  Speaker 1: to be able to fire antibiotics at a bacterium and
• 3:31
  Speaker 1: the pathogenic bacterium. I just want to say at this point,
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  Speaker 1: we don't want to kill all bacteria. They are good bacteria.
• 3:36
  Speaker 1: The ones in our guts, the one that live on
• 3:38
  Speaker 1: our skin, we need those, right? We can't process all
• 3:41
  Speaker 1: foods without bacteria. We do need them. And the ones
• 3:43
  Speaker 1: on our skins keep us also safe. It's the bad
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  Speaker 1: harmful ones we want to kill. And the problem is
• 3:50
  Speaker 1: they're protected by a formidable defense system. And it's a
• 3:54
  Speaker 1: little bit like many of you may have seen these
• 3:56
  Speaker 1: concentric castles. So what happens is you breach through the
• 4:00
  Speaker 1: first wall, the defense of the castle, and then you
• 4:03
  Speaker 1: hit with another defense that could be a moat, so
• 4:05
  Speaker 1: it could be water. You get over the water and
• 4:08
  Speaker 1: then you're hit by cannon. It's firing cannons back at you.
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  Speaker 1: It has multiple defense layers that it's built. And for
• 4:15
  Speaker 1: those of you who are a bit more astute maybe
• 4:17
  Speaker 1: with chemistry will understand what I'm going to say next
• 4:20
  Speaker 1: is that some of these layers are like oil droplets. Okay,
• 4:24
  Speaker 1: so some of them are like oil and some of
• 4:27
  Speaker 1: them are like water. Now we have a problem. You
• 4:30
  Speaker 1: guys all know that something which mixes with water will
• 4:33
  Speaker 1: not mix with oil and vice versa. Okay? So how
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  Speaker 1: do I make a molecule? How do I make a drug?
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  Speaker 1: That it will go into oil, and then it will
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  Speaker 1: get through the oil, and then it will get into
• 4:44
  Speaker 1: the water, it will get through the water, then it
• 4:46
  Speaker 1: will get into the oil, and then it will get inside.
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  Speaker 1: Does that sort of make sense? We have a real problem.
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  Speaker 1: They've developed these massive defense systems. And then not only that,
• 4:57
  Speaker 1: they have developed pumps. So if the antibiotic starts to
• 5:01
  Speaker 1: get inside, they have these pumps that start pumping them
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  Speaker 1: back out. And when they sense they're under attack by antibiotics,
• 5:08
  Speaker 1: they start producing more pumps, right? So they are very,
• 5:12
  Speaker 1: very sophisticated. But at the end of the day, there's
• 5:15
  Speaker 1: still no brain, eyes, nose, or mouth. So everything that
• 5:18
  Speaker 1: is happening is happening at the level of individual molecules.
• 5:21
  Speaker 1: And it's this that I'm trying to, not particularly well,
• 5:24
  Speaker 1: I guess, understand. And so I just wanted to focus
• 5:29
  Speaker 1: on some work we've been doing on the very outside defense,
• 5:34
  Speaker 1: trying to understand how this works. I won't go into
• 5:37
  Speaker 1: the details too much, but what I wanted to say
• 5:40
  Speaker 1: is some of you may ask, well, how do you
• 5:41
  Speaker 1: build your computer models? Where does the information come from?
• 5:45
  Speaker 1: That information comes from my colleagues who do go into
• 5:48
  Speaker 1: the lab, who do wear white coats, and they do
• 5:51
  Speaker 1: the experiments, and they come out and they give me information.
• 5:54
  Speaker 1: This could be from microscopes. They say, hey, Simon, this
• 5:57
  Speaker 1: goes here, this moves around here, this works best at
• 6:00
  Speaker 1: this temperature, or I need this much salt, or I
• 6:03
  Speaker 1: need this to get this to work, for example. They
• 6:06
  Speaker 1: might be doing x-rays. So it turns out you can
• 6:09
  Speaker 1: fire x-rays at proteins, just like we can get x-rays
• 6:12
  Speaker 1: if we break our arm or something. We can also
• 6:14
  Speaker 1: get x-rays of proteins and see what they look like.
• 6:17
  Speaker 1: So in my lab, we put all of that together
• 6:20
  Speaker 1: and we build these huge computer models And I'll just
• 6:23
  Speaker 1: show you a little movie, which is not particularly informative,
• 6:26
  Speaker 1: I guess, and just gives you an idea of the
• 6:28
  Speaker 1: scale of the model. So this is the biggest simulation
• 6:32
  Speaker 1: of a bacterial outer membrane ever done as far as
• 6:35
  Speaker 1: I know. And this was done in my lab in 2022.
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  Speaker 1: And so this was, of course, something we had to
• 6:42
  Speaker 1: do on the supercomputer in Edinburgh. This will have taken
• 6:45
  Speaker 1: about three weeks of calculation. So you just let the
• 6:48
  Speaker 1: calculation run for three weeks, day and night. Then you
• 6:51
  Speaker 1: get the data out and then it's analyzed. Okay, so
• 6:55
  Speaker 1: what does it do? What do we learn from it? Okay,
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  Speaker 1: I just played this movie here. So what we learn
• 7:01
  Speaker 1: is the different colored balls that you can see, these
• 7:03
  Speaker 1: are antibiotics. The balls are flying around and the blue
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  Speaker 1: things are proteins. They're hitting the proteins. You cannot get
• 7:11
  Speaker 1: this level of detail from any experimental method alone. You
• 7:14
  Speaker 1: have to combine experimental methods. And so what we're showing
• 7:18
  Speaker 1: is we can see the antibiotic is getting stuck here
• 7:22
  Speaker 1: or it's getting stuck here. This is what we need
• 7:24
  Speaker 1: to change to ease its path in. So this is
• 7:27
  Speaker 1: the kind of information we can get from these calculations. Okay,
• 7:31
  Speaker 1: the diagram on the top right, you can see four
• 7:35
  Speaker 1: in blue, these are four proteins, and the different colored
• 7:38
  Speaker 1: molecules you can see, they are antibiotics. We then go
• 7:41
  Speaker 1: back and we study in detail, where is the antibiotic
• 7:45
  Speaker 1: sticking to the protein? And then we can go back
• 7:47
  Speaker 1: and we can talk to our medicinal chemistry friends, and
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  Speaker 1: we can say, when you make the antibiotic, change it
• 7:54
  Speaker 1: here or here or here, and then from there we
• 7:57
  Speaker 1: can learn something. And this sort of approach that we
• 8:00
  Speaker 1: use in my lab is very different to the traditional approach.
• 8:04
  Speaker 1: And this is what computational microbiology is. So the traditional approach,
• 8:08
  Speaker 1: you can see a picture there. In blue, you see
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  Speaker 1: one protein. In yellow, you see one antibiotic or potential
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  Speaker 1: drug molecule. And this is traditionally what has been done
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  Speaker 1: until a few years ago where we tried to, within
• 8:21
  Speaker 1: my group, we tried to shake the field up a
• 8:23
  Speaker 1: little bit. We sort of said, and I come from
• 8:25
  Speaker 1: a chemistry background, so I sort of had an understanding
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  Speaker 1: of that. And then sort of when I moved to
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  Speaker 1: Oxford the first time around as a contracted researcher, it
• 8:35
  Speaker 1: sort of occurred to me that biology is not the
• 8:37
  Speaker 1: same as chemistry. In chemistry, I have my test tube.
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  Speaker 1: I know exactly what I put in there because I
• 8:44
  Speaker 1: put it in. I shake it around and something happens
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  Speaker 1: as a function of what I put in that test tube.
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  Speaker 1: Biology and nature is not like that. I do not
• 8:54
  Speaker 1: know what is in there. Sometimes I know what's in there,
• 8:57
  Speaker 1: but I don't know what it's doing. And so my
• 8:59
  Speaker 1: philosophy now with my group is to put in as
• 9:03
  Speaker 1: much information as we can, wherever it comes from, even
• 9:06
  Speaker 1: if we think it's not relevant. It may be relevant
• 9:09
  Speaker 1: in a way we don't understand. Nature is not something
• 9:13
  Speaker 1: we can understand so easily. And so our philosophy has
• 9:17
  Speaker 1: been to put in as much detail as we can
• 9:19
  Speaker 1: into the calculations and something may emerge then in a
• 9:24
  Speaker 1: way that is unexpected. And there was a privilege a
• 9:29
  Speaker 1: couple of years ago that the 30th anniversary edition of
• 9:33
  Speaker 1: the journal Structure then invited a review article on computational microbiology,
• 9:38
  Speaker 1: which I felt like was a bit of a validation
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  Speaker 1: that this field is now sort of arriving as something
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  Speaker 1: that's being taken seriously and people are thinking maybe this
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  Speaker 1: is the way to do things. Okay, so I'm just
• 9:51
  Speaker 1: gonna sort of now focus on maybe one of these
• 9:54
  Speaker 1: watery regions. So I said that there were oily regions
• 9:57
  Speaker 1: and then there's watery regions. The watery regions is called
• 10:01
  Speaker 1: the periplasm. It's densely packed with proteins. And so what
• 10:05
  Speaker 1: we're trying to understand in my group is what is
• 10:08
  Speaker 1: interacting with what, and sort of, what Diba said, I
• 10:11
  Speaker 1: was gonna talk about some of the challenges, and this
• 10:13
  Speaker 1: is a challenge which I'm going to sort of talk
• 10:15
  Speaker 1: to you about now. Here is a movie from one
• 10:17
  Speaker 1: of these simulations. So this was done a few years
• 10:19
  Speaker 1: ago now when I was still at the University of Southampton.
• 10:22
  Speaker 1: It was done by a very talented postdoc who's now
• 10:24
  Speaker 1: moved back to Brazil. And you can see there's a
• 10:27
  Speaker 1: lot going on in this movie. The red stuff is moving,
• 10:29
  Speaker 1: the orange stuff is moving, the bluey bits are moving,
• 10:32
  Speaker 1: everything is moving around. And the water and the salt
• 10:35
  Speaker 1: I've taken out just to make things easy to see, right?
• 10:38
  Speaker 1: And so what we have to do now is, on
• 10:41
  Speaker 1: the one hand, I've said, sort of given myself a
• 10:44
  Speaker 1: pat on the back and said, I'm very clever, I'm
• 10:45
  Speaker 1: putting all of this detail in, no one else is
• 10:47
  Speaker 1: doing that. But I've kind of created a problem for myself, right?
• 10:51
  Speaker 1: Because now I have to analyze what comes out. So
• 10:54
  Speaker 1: if I build this thing where I put in all
• 10:56
  Speaker 1: my molecules, my antibiotics, my proteins, all the other stuff
• 11:00
  Speaker 1: that I think exists in real life, lots of conditions,
• 11:03
  Speaker 1: different temperatures, different salt conditions, and I try and understand it,
• 11:08
  Speaker 1: this is what the graphs look like. So hopefully you'll
• 11:12
  Speaker 1: agree with me that it's very difficult to tell the
• 11:14
  Speaker 1: difference between the one on the left and the one
• 11:16
  Speaker 1: on the right. Apparently, mathematically, there is a difference. There
• 11:20
  Speaker 1: is some signal in that noise, but to my eyes,
• 11:23
  Speaker 1: they just look like a messy noise, basically. And so
• 11:27
  Speaker 1: what we're doing now is we're collaborating with Arvind Ramanathan
• 11:32
  Speaker 1: in Argonne National Laboratory in the USA. He's an artificial
• 11:36
  Speaker 1: intelligence expert. And what we're trying to do is we're
• 11:39
  Speaker 1: trying to use this kind of thing. So when you
• 11:42
  Speaker 1: guys pick up your mobile phone, and you take a photograph,
• 11:45
  Speaker 1: it recognizes the face, right? All the algorithms now are clever.
• 11:49
  Speaker 1: They know this is a face. There is something in
• 11:51
  Speaker 1: there that recognizes the signal. These are two eyes. This
• 11:54
  Speaker 1: is a nose. This is a mouth. Can I use
• 11:57
  Speaker 1: the same technology to find the signal in all of
• 11:59
  Speaker 1: this noise in my data? And it turns out we
• 12:02
  Speaker 1: probably can. So it's a very fruitful collaboration that we
• 12:05
  Speaker 1: have with Avent. And just sort of to take that
• 12:09
  Speaker 1: on one step further, this is some new stuff that
• 12:12
  Speaker 1: I'm really excited about. Actually the last is quite timely
• 12:14
  Speaker 1: because the latest, the very newest data just came out
• 12:19
  Speaker 1: two days ago from this. What we've set up with
• 12:21
  Speaker 1: Arvind is we do the computer simulations in my laboratory
• 12:25
  Speaker 1: in Oxford. We then email the results directly to the
• 12:29
  Speaker 1: robot in Argonne. The robot makes the antibiotic. tests it
• 12:37
  Speaker 1: on a plate with the bacteria, then does an image analysis,
• 12:43
  Speaker 1: how many are dead, how many are alive, feeds that
• 12:46
  Speaker 1: into our artificial intelligence algorithm, improves the design of the antibiotic,
• 12:51
  Speaker 1: back to my laboratory for more simulation to say we
• 12:54
  Speaker 1: think this is how it works, then back to the robot.
• 12:57
  Speaker 1: So this is just something that's been set up in
• 12:59
  Speaker 1: the last few days. And I guess, ultimately, the hope
• 13:03
  Speaker 1: is that you can take me out of the equation.
• 13:05
  Speaker 1: At some point, the artificial intelligence will learn enough that
• 13:09
  Speaker 1: you no longer need us to do the simulations. It
• 13:12
  Speaker 1: will have enough to carry on and improve without us.
• 13:15
  Speaker 1: But for now, it needs our mechanistic insights. And then
• 13:19
  Speaker 1: to kind of finish off, just to answer one of
• 13:24
  Speaker 1: the queries you put at me about what the future
• 13:27
  Speaker 1: might look like. Well, I think... I think some of
• 13:31
  Speaker 1: the questions we perhaps should ask going forward is about
• 13:36
  Speaker 1: sort of, even if I work out an antibiotic, why
• 13:39
  Speaker 1: is it that one antibiotic might work for me but
• 13:42
  Speaker 1: not for you? Why is this? And oftentimes, why is
• 13:46
  Speaker 1: it that some bacteria will be effective, sorry, some antibiotics
• 13:51
  Speaker 1: will be effective against this bacterium but not this one?
• 13:55
  Speaker 1: Why is that? You know, and sometimes when you go
• 13:58
  Speaker 1: to the doctor and you have a bacterial infection, it
• 14:00
  Speaker 1: takes them two or three goes to get the right
• 14:02
  Speaker 1: antibiotic for you. Why is that? And I think these
• 14:06
  Speaker 1: are the kind of questions that we should be thinking
• 14:09
  Speaker 1: about now moving forward. And it turns out that this
• 14:13
  Speaker 1: is within the realm of something called personalized medicine. So
• 14:16
  Speaker 1: we're almost at the stage where it's beginning to become
• 14:20
  Speaker 1: possible to deliver personal medicine for tailored for individuals. And
• 14:25
  Speaker 1: this really comes from enormous steps in genetics, and our
• 14:30
  Speaker 1: ability to sequence the human genome. So to find the
• 14:33
  Speaker 1: secrets which are within our genomes. So for example, my
• 14:37
  Speaker 1: brother is in the audience, we have the same parents,
• 14:39
  Speaker 1: but what makes him different to me? Right? So this
• 14:43
  Speaker 1: secret is in our DNA. And for example, I have
• 14:47
  Speaker 1: friends and colleagues at Oxford Nanopore Technologies, a wonderful world-leading
• 14:51
  Speaker 1: company that's come out of Oxford, and they are now
• 14:54
  Speaker 1: managing to do this. You can do it on something
• 14:56
  Speaker 1: as small as a USB stick now. They can take
• 14:59
  Speaker 1: that and they can sequence genomes using this technology. And
• 15:02
  Speaker 1: I think that's the future. The future is going to
• 15:05
  Speaker 1: be not just a sort of a widespread generic medicine,
• 15:09
  Speaker 1: but a tailored personalized medicine. And so... With that, I
• 15:15
  Speaker 1: kind of want to leave you with an outlook that
• 15:19
  Speaker 1: from my side, my work and sort of I can
• 15:23
  Speaker 1: see in the next few years, what we're trying to
• 15:26
  Speaker 1: do is we are trying to use our simulations as
• 15:29
  Speaker 1: computational assays. So the doctors, the white coat wearing scientists,
• 15:35
  Speaker 1: they do assays. I want to use my computer to
• 15:39
  Speaker 1: do assays, but to do that I have to put
• 15:42
  Speaker 1: in as much detail as I can. I firmly think that.
• 15:45
  Speaker 1: I think it's not sufficient just to say, here's my drug,
• 15:49
  Speaker 1: here's my target, this is what I'm going to simulate.
• 15:52
  Speaker 1: I have to put in everything else as well. And
• 15:55
  Speaker 1: that's not so easy to do because sometimes we don't
• 15:57
  Speaker 1: know what's in there. But we can keep moving and
• 16:00
  Speaker 1: going forward and getting information from as many collaborators and
• 16:03
  Speaker 1: people who are willing to provide that as possible. So
• 16:07
  Speaker 1: that's sort of what I wanted to say about the science.
• 16:09
  Speaker 1: But what's also incredibly important to me is, and I
• 16:12
  Speaker 1: think this also chimes with the ideals of the Muslim Institute,
• 16:17
  Speaker 1: is to spread the word of science, to spread the
• 16:20
  Speaker 1: word of bringing science to wider communities. And if you
• 16:25
  Speaker 1: might indulge me for a second, I think sometimes, oftentimes,
• 16:29
  Speaker 1: our communities have been less willing to do that because
• 16:32
  Speaker 1: oftentimes we want our children to be doctors. about how
• 16:36
  Speaker 1: many of us want our children to be scientists, the
• 16:38
  Speaker 1: ones who make the medicines and discover them so that
• 16:40
  Speaker 1: the doctors can apply them. And I think hopefully the pandemic,
• 16:45
  Speaker 1: if anything good has come out of it, some of
• 16:47
  Speaker 1: the things can be that this learning is also incredibly important.
• 16:51
  Speaker 1: And so to spread some of this knowledge, one of
• 16:54
  Speaker 1: the things I've been doing recently with my colleague, Tim
• 16:57
  Speaker 1: Coulson at Oxford is we've set up a podcast. It's
• 17:01
  Speaker 1: called Science of the Times. And you can find us
• 17:04
  Speaker 1: on YouTube, Spotify, Apple podcasts. And what we try to
• 17:09
  Speaker 1: do is bring science stories to the wider public in
• 17:13
  Speaker 1: a way that's not patronizing, but still accessible. And so
• 17:17
  Speaker 1: if anyone wants to know any more about that, give
• 17:19
  Speaker 1: me a shout. I'd be more than happy to chat
• 17:21
  Speaker 1: to you. And I bought some flyers if anyone's interested.
• 17:24
  Speaker 1: And indeed, if you have ideas of science stories or
• 17:27
  Speaker 1: there's things about science that you want us to cover,
• 17:29
  Speaker 1: that you want to know more about, then please email in.
• 17:32
  Speaker 1: We're looking for engagement, actually. And with that, I want
• 17:36
  Speaker 1: to acknowledge the people. So it's my privilege to sit
• 17:40
  Speaker 1: here and talk to you about the science, but of
• 17:43
  Speaker 1: course I don't do the science. The science is done
• 17:45
  Speaker 1: by my team. And so I acknowledge those members of
• 17:49
  Speaker 1: my team who are with me right now at Oxford,
• 17:52
  Speaker 1: but of course the work I've presented has come over
• 17:55
  Speaker 1: 20 years of research and I can't possibly name them all.
• 17:59
  Speaker 1: So this has included undergraduate students, PhD students, post-doctoral researchers,
• 18:05
  Speaker 1: and I'm grateful to every single one of them for
• 18:07
  Speaker 1: their diligence and hard work and for putting up with
• 18:11
  Speaker 1: me as a crazy at times boss. Also collaborators, none
• 18:16
  Speaker 1: of us is an island, no work is done in isolation.
• 18:19
  Speaker 1: I have wonderful collaborators all around the world. Some of
• 18:22
  Speaker 1: them are computational, some of them are are experimental. And
• 18:29
  Speaker 1: what I love about my collaborators is when we don't agree,
• 18:32
  Speaker 1: they never say the calculations must be wrong, right? I
• 18:37
  Speaker 1: never say the experiments must be wrong. We always think
• 18:39
  Speaker 1: maybe we've measured the wrong things. So let's meet in
• 18:42
  Speaker 1: the middle. And that open-mindedness has been wonderful. And every
• 18:45
  Speaker 1: single one of those collaborators is the same. And finally,
• 18:49
  Speaker 1: I want to also acknowledge my funders. Science is not
• 18:53
  Speaker 1: possible without money, I'm afraid. And so the ones named
• 18:57
  Speaker 1: at the bottom have supported my research for many years.
• 19:00
  Speaker 1: And Biosim is this organization that provides computing time on
• 19:04
  Speaker 1: the national resource in Edinburgh. And before just, this is
• 19:09
  Speaker 1: my final slide, but before finishing, I also wanted to
• 19:13
  Speaker 1: A special acknowledgement, really, for my support team. And if
• 19:16
  Speaker 1: you like my dream team, here they are. And from
• 19:20
  Speaker 1: the bottom of my heart, thank you. And thank you.
• 19:22
  Speaker 1: Thank you. Thank you. Hey, welcome back. I hope you
• 19:51
  Speaker 1: enjoyed the lecture. I must say it was, uh, an exciting, um,
• 19:56
  Speaker 1: and at the same time, nerve wracking thing to do.
• 19:59
  Speaker 1: The surroundings were beautiful. Bradford city hall really is a wonderful,
• 20:04
  Speaker 1: wonderfully elaborate and beautiful building. Um, the Lord mayor of
• 20:08
  Speaker 1: Bradford was in attendance. It was a great audience. Unfortunately,
• 20:12
  Speaker 1: what we don't get in the coverage is the question
• 20:15
  Speaker 1: and answer session. There were some really probing questions about
• 20:18
  Speaker 1: the use of artificial intelligence in the discovery of therapeutics,
• 20:23
  Speaker 1: the threat of antimicrobial resistance, and also some more personal
• 20:27
  Speaker 1: questions in terms of any impediments or barriers I faced
• 20:32
  Speaker 1: in my own career due to ticking various minority boxes,
• 20:36
  Speaker 1: I guess. And if anyone is interested in any of
• 20:39
  Speaker 1: my responses to those and please do get in touch,
• 20:42
  Speaker 1: I'd be happy to share that with you. I must say,
• 20:45
  Speaker 1: I must say what was most nerve wracking for me
• 20:47
  Speaker 1: was the fact that my parents and my brother were
• 20:50
  Speaker 1: also in attendance. And when it got to the question
• 20:52
  Speaker 1: and answer session, I was petrified that my dad would
• 20:55
  Speaker 1: hold up a standard plug and ask me if I
• 20:58
  Speaker 1: know yet know how to change the fuse in that.
• 21:01
  Speaker 1: But luckily he desisted and that didn't happen. In the end,
• 21:04
  Speaker 1: it was a wonderful event and I'm very grateful for
• 21:07
  Speaker 1: the Muslim Institute and of course for you for listening
• 21:09
  Speaker 1: and for your attention. As always, if you liked what
• 21:12
  Speaker 1: you've seen and heard here, then please do subscribe, tell
• 21:16
  Speaker 1: your friends, like, send us some comments and any ideas
• 21:20
  Speaker 1: you have for future episodes. Okay guys, until the next time,
• 21:23
  Speaker 1: when Tim will be back, take care.